Method for automatically identifying organs endangered by radiotherapy in CT image based on deep semantic network

Abstract

The embodiment of the invention provides a method for automatically identifying organs endangered by radiotherapy in a CT image based on a deep semantic network. The method comprises the following steps: S1, preprocessing a CT three-dimensional image; s2, obtaining a part to which each two-dimensional image in the CT three-dimensional image belongs; s3, respectively constructing deep semantic segmentation models for the pelvic cavity, the abdomen, the chest, the head and the neck; s4, inputting the two-dimensional images belonging to the pelvic cavity, the belly, the chest, the head and the neck into a trained deep semantic segmentation model for the corresponding pelvic cavity, the belly, the chest, the head and the neck to identify organs endangered by respective radiotherapy; and S5, combining results output by the deep semantic segmentation models of the pelvic cavity, the abdomen, the chest, the head and the neck. According to the method, artificial intelligence-assisted radiotherapy endangered organ contour sketching is implemented in the working process of radiotherapy planning, preoperative evaluation of surgical operation and operation planning, and the working efficiencyof medical workers can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a method for automatically identifying organs at risk for radiotherapy in CT images based on a deep semantic network. Background technique [0002] In the field of medicine, precision radiation therapy technology has greatly improved the survival rate of cancer patients. However, these advanced treatment methods require not only accurate judgment of the contour of the target tumor, but also accurate recognition of the contours of vital organs around the tumor, so that these organs can be protected during radiation therapy. In addition, in the field of surgical applications, accurate preoperative assessment and standardized radical surgery are important measures to improve the efficacy of tumor diagnosis and treatment. Organ contour recognition based on CT image data can help doctors complete surgical planning quickly, accurately and with high consistency. Steps ...

AI Technical Summary

Problems solved by technology

Manual drawing by doctors has the following disadvantages: 1. The drawing efficiency is low; 2. It relies heavily on the doctor's clinical experience; 3. The repeatability is poor, and the results drawn by different doctors at different times and under different conditions are inconsistent.

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